{"id":"W3209823934","doi":"10.32920/ryerson.14649783.v1","title":"Development of UAV derivative MDO methodology for flight simulation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Aircraft Design and Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Multidisciplinary design optimization; Process (computing); Flight simulator; Derivative (finance); Computer science; Simulation; Time derivative; Engineering; Multidisciplinary approach; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002767733,0.0001739511,0.000321781,0.00004336785,0.000059121,0.000008500449,0.0002615104,0.0002764689,0.000600466],"category_scores_gemma":[0.0003408786,0.0001514933,0.00007808279,0.0001023258,0.0001366053,0.00006232173,0.0009900241,0.0001520352,0.00001068035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001518296,"about_ca_system_score_gemma":0.00003966378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001320391,"about_ca_topic_score_gemma":0.00006386895,"domain_scores_codex":[0.9988497,0.00005247292,0.0003445108,0.0004332025,0.0001422437,0.0001778952],"domain_scores_gemma":[0.9990527,0.0003990651,0.0002030156,0.0002952179,0.0000256806,0.0000242923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007689151,0.0001942837,0.002012557,0.000237204,0.0001895669,0.000004835631,0.004356348,0.2973307,0.1993279,0.002138267,0.000333936,0.4937975],"study_design_scores_gemma":[0.0002988379,0.00004524424,0.004264174,0.0000560252,0.00002816926,9.166284e-7,0.001242732,0.01258184,0.9251108,0.03972618,0.01617716,0.0004679006],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06856576,0.0000426723,0.9288222,0.0001110594,0.0001220018,0.0004502524,0.000003975387,0.0001030029,0.001779097],"genre_scores_gemma":[0.3480157,0.000007598486,0.6515381,0.00003650241,0.000005634658,0.00008321126,0.00002614649,0.000008950189,0.0002780981],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7257829,"threshold_uncertainty_score":0.6574681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1339321546614659,"score_gpt":0.3571032787808954,"score_spread":0.2231711241194295,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}